pip install langgraph==0.2.53 langgraph-checkpoint==2.0.6 langgraph-sdk==0.1.36 langchain-groq langchain-community langgraph-checkpoint-sqlite==2.0.1 tavily-python streamlit

import osimport sqlite3from langgraph.graph import StateGraphfrom langchain_core.messages import SystemMessage, HumanMessagefrom langchain_groq import ChatGroqfrom tavily import TavilyClientfrom langgraph.checkpoint.sqlite import SqliteSaverfrom typing import TypedDict, Listfrom pydantic import BaseModelimport streamlit as st# Set API Keysos.environ['TAVILY_API_KEY'] = "your_tavily_key"os.environ['GROQ_API_KEY'] = "your_groq_key"# Initialize Database for Checkpointingsqlite_conn = sqlite3.connect("checkpoints.sqlite", check_same_thread=False)memory = SqliteSaver(sqlite_conn)# Initialize Model and Tavily Clientmodel = ChatGroq(model="Llama-3.1-8b-instant")tavily = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])

class AgentState(TypedDict):    topic: str    drafts: List[str]    content: List[str]    critiques: List[str]    refined_summaries: List[str]    headings: List[str]

BROWSING_PROMPT = """You are an AI news researcher tasked with finding the latest news articles on given topics. Generate up to 3 relevant search queries."""WRITER_PROMPT = """You are an AI news summarizer. Write a detailed summary (1 to 2 paragraphs) based on the given content, ensuring factual correctness, clarity, and coherence."""CRITIQUE_PROMPT = """You are a teacher reviewing draft summaries against the source content. Ensure factual correctness, identify missing or incorrect details, and suggest improvements.----------Content: {content}----------"""REFINE_PROMPT = """You are an AI news editor. Given a summary and critique, refine the summary accordingly.-----------Summary: {summary}"""HEADING_GENERATION_PROMPT = """You are an AI news summarizer. Generate a short, descriptive headline for each news summary."""

from pydantic import BaseModelclass Queries(BaseModel):    queries: List[str]class News(BaseModel):    news: List[str]

def browsing_node(state: AgentState):    queries = model.with_structured_output(Queries).invoke([        SystemMessage(content=BROWSING_PROMPT),        HumanMessage(content=state['topic'])    ])    content = state.get('content', [])    for q in queries.queries:        response = tavily.search(query=q, max_results=2)        for r in response['results']:            content.append(r['content'])    return {"content": content}

def writing_node(state: AgentState):    content = "\n\n".join(state['content'])    news = model.with_structured_output(News).invoke([        SystemMessage(content=WRITER_PROMPT),        HumanMessage(content=content)    ])    return {"drafts": news.news}

def reflection_node(state: AgentState):    content = "\n\n".join(state['content'])    critiques = []    for draft in state['drafts']:        response = model.invoke([            SystemMessage(content=CRITIQUE_PROMPT.format(content=content)),            HumanMessage(content="draft: " + draft)        ])        critiques.append(response.content)    return {"critiques": critiques}

def refine_node(state: AgentState):    refined_summaries = []    for summary, critique in zip(state['drafts'], state['critiques']):        response = model.invoke([            SystemMessage(content=REFINE_PROMPT.format(summary=summary)),            HumanMessage(content="Critique: " + critique)        ])        refined_summaries.append(response.content)    return {"refined_summaries": refined_summaries}

def heading_node(state: AgentState):    headings = []    for summary in state['refined_summaries']:        response = model.invoke([            SystemMessage(content=HEADING_GENERATION_PROMPT),            HumanMessage(content=summary)        ])        headings.append(response.content)    return {"headings": headings}

# Define Streamlit appst.title("News Summarization Chatbot")# Initialize session stateif "messages" not in st.session_state:    st.session_state["messages"] = []# Display past messagesfor message in st.session_state["messages"]:    with st.chat_message(message["role"]):        st.markdown(message["content"])# Input field for useruser_input = st.chat_input("Ask about the latest news...")thread = 1if user_input:    st.session_state["messages"].append({"role": "user", "content": user_input})    with st.chat_message("assistant"):        loading_text = st.empty()        loading_text.markdown("Thinking...")        builder = StateGraph(AgentState)        builder.add_node("browser", browsing_node)        builder.add_node("writer", writing_node)        builder.add_node("reflect", reflection_node)        builder.add_node("refine", refine_node)        builder.add_node("heading", heading_node)        builder.set_entry_point("browser")        builder.add_edge("browser", "writer")        builder.add_edge("writer", "reflect")        builder.add_edge("reflect", "refine")        builder.add_edge("refine", "heading")        graph = builder.compile(checkpointer=memory)        config = {"configurable": {"thread_id": f"{thread}"}}        for s in graph.stream({"topic": user_input}, config):            # loading_text.markdown(f"{st.session_state['loading_message']}")            print(s)                s = graph.get_state(config).values        refined_summaries = s['refined_summaries']        headings = s['headings']        thread+=1        # Display final response        loading_text.empty()        response_text = "\n\n".join([f"{h}\n{s}" for h, s in zip(headings, refined_summaries)])        st.markdown(response_text)        st.session_state["messages"].append({"role": "assistant", "content": response_text})